Site estimation based on crowd sourced data

ABSTRACT

Crowd sourced data from mobile devices may be used to estimate site locations. In addition, machine learning models may be used to filter out any inaccurate crowd sourced data before using algorithms to estimate the cell site location. An apparatus may include a processor and a memory coupled with the processor that effectuates operations. The operations may include receiving data associated with a location of plurality of devices that have connected with a base station in a geographic area; determining a machine learning model to apply to the received data based on the type of data, wherein the machine learning model is a clustering model; performing the clustering model on the received data; based on the performing the clustering model on the received data, obtaining representative data that excludes outliers in the received data; based on the representative data, determining a location of the base station in the geographic area; and sending a message with the location of the base station.

BACKGROUND

Mobile devices such as cellular telephones, PDAs, etc. are proliferatinglike never before. Users may install applications to access severaldifferent provider networks and can access voice, text, and multimediadata from other network entities such as servers and other mobiledevices.

These mobile devices additionally include Global Positioning System(GPS) receivers, which provides for a host of location-based services(LBS). Location estimation of mobile devices is important for obtaininglocation tagged network failure data for system optimization,location-based services, 911 services, and a variety of other locationenhanced applications.

This background information is provided to reveal information believedby the applicant to be of possible relevance. No admission isnecessarily intended, nor should be construed, that any of the precedinginformation constitutes prior art.

SUMMARY

Crowd sourced data from mobile devices may be used to estimate sitelocations. In addition, machine learning models may be used to filterout any inaccurate crowd sourced data before using algorithms toestimate the cell site location.

In an example, an apparatus may include a processor and a memory coupledwith the processor that effectuates operations. The operations mayinclude receiving data associated with a location of plurality ofdevices that have connected with a base station in a geographic area;determining a machine learning model to apply to the received data basedon the type of data, wherein the machine learning model is a clusteringmodel; performing the clustering model on the received data; based onthe performing the clustering model on the received data, obtainingrepresentative data that excludes outliers in the received data; basedon the representative data, determining a location of the base stationin the geographic area; and sending a message with the location of thebase station.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. Furthermore,the claimed subject matter is not limited to limitations that solve anyor all disadvantages noted in any part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale.

FIG. 1 illustrates an exemplary system for site estimation based oncrowd sourced data;

FIG. 2 illustrates an exemplary method for site estimation based oncrowd sourced data;

FIG. 3A illustrates exemplary map view for user equipment (UE)locations;

FIG. 3B illustrates exemplary map view for UE locations;

FIG. 4 illustrates an exemplary system using trilateration;

FIG. 5 illustrates a schematic of an exemplary network device; and

FIG. 6 illustrates an exemplary communication system that provideswireless telecommunication services over wireless communicationnetworks.

DETAILED DESCRIPTION

By estimating where other network operators have built cell sites andwhere they are adding new sites, an operator can discover its advantagesor disadvantages compared with its competitors. This can help with newdesign plans to enhance areas that other carriers are now coveringbetter and improve network coverage and expand new generationtechnologies. As a competitor brings new sites online, it may be helpfulto understand what advantages this provides. This can help with newnetwork design and investing plans to enhance the network performancefor better user experience.

Conventional drive testing to determine base station (e.g., cell site)locations may only use the data available where drives were conducted,which may leave a considerable amount of the network untested.Additionally, it is only practical to collect data via drive testing orthe like a few times a year so this data may become out of date ratherquickly during major deployments. However, crowd sourced data may becollected continuously in near real-time and may be used to determinebase station locations.

Even though using crowd sourced data may generally lead to morereal-time testing, the crowd sourced data may include inaccurateinformation, such as incorrect cell identity, incorrectlatitude/longitude (lat/lon) location information, or inaccurate timingadvance (TA) measurements. Using such inaccurate information may lead tocell site location estimation errors that would be too inaccurate touse. Therefore, as disclosed herein, machine learning models may be usedto filter out the inaccurate information before using algorithms toestimate the cell site location.

FIG. 1 illustrates an exemplary system for site estimation based oncrowd sourced data, among other things. System 100 may include network106, user equipment (UE) 101, base station 102, UE 103, or UE 104. Thedevices of system 100 may be communicatively connected with each otherand network 106 (e.g., a cloud network). The UEs may include a laptop,tablet, autonomous vehicle (e.g., SAE Intl level 3 to level 5automation), mobile phone, or internet of things device, among otherdevices. Vehicles may include aerial, ground, or water-based vehicles.Server 105 may obtain location related data from the plurality ofdevices of system 100 and use machine learning or other algorithms toassist in accurately determining the location of base station 102.

FIG. 2 illustrates an exemplary method for site estimation based oncrowd sourced data, among other things. At step 111, server 105 mayreceive data associated with a location of plurality of devices ofsystem 100 in a geographic area. The location data may be gathered fromthe plurality of UE of system 100 via an installed application. Forexample, UE 101 may have an app that is installed to enable the use of aservice. The user of UE 101 may authorize the use of data collectedduring the use of the service (e.g., a speed test service or drive testbenchmarking tool service). This gathered application data may includethe latitude for UE 101, the longitude for UE 101, the distance betweenUE 101 and base station 102 (e.g., eNodeB cell site), or the timingadvance, along with other data. The distance between UE 101 and basestation 102 may be calculated based on the timing advance.

With continued reference to FIG. 2, at step 112, a machine learningmodel may be determined by server 105. The machine learning model may bebased on the type of data received. The type of data may include RFinformation, device type information, or distance information, amongother things. In this example scenario, the machine learning model maybe a clustering model, such as density-based spatial clustering ofapplications with noise (DBSCAN). DBSCAN is a density-based clusteringnon-parametric algorithm: given a set of points in some space, it groupstogether points that are closely packed together (e.g., points with manynearby neighbors), marking as outliers points that lie alone inlow-density regions (whose nearest neighbors are too far away).

With continued reference to FIG. 2, at step 113, server 105 may applythe clustering model, to the data of step 111. At step 114, based on theapplying of the clustering model, excluding outliers in order to obtaina cluster of data (herein referred to as representative data) that maybe subsequently used for determining a location of base station 102. Asshown in Table 1, clustering labels may be associated with each UE ofsystem 100 within a geographic area. Table 1 lists some sample data fromone cell site collected by using crowd sourcing (e.g., data gatheredfrom one or more applications). This table of crowd sourced data of eachdevice of the plurality of devices of system 100 may include thelocations (e.g., UE_LAT/UE_LON), the estimated distance between eachdevice and base station 102, or the timing advances (TA). The“clustering labels” may be considered the output of the clusteringmachine learning model, which is not in the crowd sourced data.

With continued reference to step 114 and Table 1, FIG. 3A and FIG. 3Billustrate exemplary map view for user equipment (UE) locationsassociated within geographic area 107. Applying the clustering model toUE_LAT/UE_LON, these UE locations are clustered into 3 clusters shown in“Clustering label” of Table 1. FIG. 3A shows the map view of the UEswithin a geographic area (e.g., geographic area 107). It is seen thatmost of the UEs are clustered as cluster_0 except several UE locationsare far away from the majority labeled as cluster_1 and cluster_2respectively. If the data points with cluster_1 and cluster_2 are usedto calculate the cell site location, the wrong cell site location wouldbe around lat/lon (30.89597, −115.21337), which is 15657 meters far awayfrom the actual cell site location (30.75554, −115.21337). Hence, theseoutliers should be excluded. The data points in the cluster having themost data points to perform the trilateration algorithm should beselected as the representative data to estimate the cell site location(e.g., location of base station 102). FIG. 3B shows cluster_0, whichshould be selected in subsequent location determinations of base station102. Note that other outlier detection models and information in thecrowd source such as radio frequency reference signal received power (RFRSRP), reference signal received quality (RSRQ), downlink throughput,uplink throughput, or latency can also apply to remove the inaccuratedata points. For example, if RF information is clustered in threeclusters, then the cluster with the most data points will be used andthe others excluded. Further, RF information can also be combined withUE locations to perform the clustering. The cluster with the fewest datapoints may be excluded since they may be the outliers or they are notrepresentative data points from the RF perspective view.

TABLE 1 Anonymized DISTANCE Clustering Device ID UE_LAT UE_LON (m) TAlabel A 30.75435 −115.20506 312.500 4.0 0 B 30.75234 −115.21069 156.2502.0 0 C 30.75118 −115.21268 546.875 7.0 0 D (e.g., 30.75495 −115.2124278.125 1.0 0 UE 101) . . . . . . . . . . . . . . . E (e.g., 30.62975−114.97969 156.25 2.0 1 UE 104) F 30.89585 −115.20237 0.00 0.0 2 G(e.g., 30.89902 −115.19189 0.00 0.0 2 UE 103)

At step 115, based on the representative data, determining the locationof base station 102. An example method for determining the location ofbase station 102 is trilateration. In a scenario in which trilaterationis used, based on the representative data, the location of the site canbe estimated by applying the trilateration algorithm to the threedifferent UE locations associated with the same cell site to calculatethe intersection of the three circles with the radius based on the TA.FIG. 4 illustrates an exemplary system using trilateration. At step 116,the determined location of base station 102 may be sent for furtherprocessing. For example, the determined base station 102 locations maybe used in a map which displays cell sites for base stations of one ormore service providers.

Additional perspective is provided below with regard to site estimationand crowd sourced date. Disclosed herein is the use of a machinelearning model, such as a clustering model, to cluster the dataassociated with the same geographic area (e.g., cell site) intodifferent groups based on the lat/lon location, TA measurement, and RFinfo. The groups containing most of the data points may be selected andfurther processed to do the base station location determinationalgorithm to get multiple intersection points. The median of theseintersection points may be determined to be the estimated the cell sitelocation. Alternatively, or in addition to, the machine learning modelmay use multiple other filtering methods to rule out inaccurate results.In a first example, the ratio of TA and signal measurement from the UEsmay be collected and used in the machine learning model. In a secondexample, location of the estimated site from existing poles, towers, orbuildings or the use of some satellite imaging recognition algorithmsmay be collected and used in the machine learning model. For this secondexample, different information may combined to use in machine learningmodels or otherwise use in or with base station location determinationalgorithms to determine the location of the cell site.

With near real-time estimates of site locations and quantities,operators can better estimate their competitors' build-out plans andmake more informed strategic decisions. The data collected from the UEmay include many events and counters that can be used for the locationcalculation. Again, Lat/long (coordinates), TA (Timing Advance), SiteID, RF measurements, quality measurements, frequency, and technologyused may be collected. The disclosed subject matter may allow forestimation accuracy of the distance from the true site location and theestimated location to be within 50 to 100 meters or less. The technology(e.g., type of UE) and the frequency may allow for understanding thetype of propagation that exists, in order to narrow and filter outoutliers. It is contemplated that the disclosed steps may occur on onedevice or distributed over multiple devices.

FIG. 5 is a block diagram of network device 300 that may be connected toor comprise a component of the systems disclosed herein, such as FIG.1-FIG. 4. Network device 300 may comprise hardware or a combination ofhardware and software. The functionality to facilitatetelecommunications via a telecommunications network may reside in one orcombination of network devices 300. Network device 300 depicted in FIG.5 may represent or perform functionality of an appropriate networkdevice 300, or combination of network devices 300, such as, for example,a component or various components of a cellular broadcast systemwireless network, a processor, a server, a gateway, a node, a mobileswitching center (MSC), a short message service center (SMSC), anautomatic location function server (ALFS), a gateway mobile locationcenter (GMLC), a radio access network (RAN), a serving mobile locationcenter (SMLC), or the like, or any appropriate combination thereof. Itis emphasized that the block diagram depicted in FIG. 5 is exemplary andnot intended to imply a limitation to a specific implementation orconfiguration. Thus, network device 300 may be implemented in a singledevice or multiple devices (e.g., single server or multiple servers,single gateway or multiple gateways, single controller or multiplecontrollers). Multiple network entities may be distributed or centrallylocated. Multiple network entities may communicate wirelessly, via hardwire, or any appropriate combination thereof.

Network device 300 may comprise a processor 302 and a memory 304 coupledto processor 302. Memory 304 may contain executable instructions that,when executed by processor 302, cause processor 302 to effectuateoperations associated with mapping wireless signal strength.

In addition to processor 302 and memory 304, network device 300 mayinclude an input/output system 306. Processor 302, memory 304, andinput/output system 306 may be coupled together (coupling not shown inFIG. 5) to allow communications between them. Each portion of networkdevice 300 may comprise circuitry for performing functions associatedwith each respective portion. Thus, each portion may comprise hardware,or a combination of hardware and software. Input/output system 306 maybe capable of receiving or providing information from or to acommunications device or other network entities configured fortelecommunications. For example, input/output system 306 may include awireless communications (e.g., 3G/4G/GPS) card. Input/output system 306may be capable of receiving or sending video information, audioinformation, control information, image information, data, or anycombination thereof. Input/output system 306 may be capable oftransferring information with network device 300. In variousconfigurations, input/output system 306 may receive or provideinformation via any appropriate means, such as, for example, opticalmeans (e.g., infrared), electromagnetic means (e.g., RF, Wi-Fi,Bluetooth®, ZigBee®), acoustic means (e.g., speaker, microphone,ultrasonic receiver, ultrasonic transmitter), or a combination thereof.In an example configuration, input/output system 306 may comprise aWi-Fi finder, a two-way GPS chipset or equivalent, or the like, or acombination thereof.

Input/output system 306 of network device 300 also may contain acommunication connection 308 that allows network device 300 tocommunicate with other devices, network entities, or the like.Communication connection 308 may comprise communication media.Communication media typically embody computer-readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. By way of example, and not limitation,communication media may include wired media such as a wired network ordirect-wired connection, or wireless media such as acoustic, RF,infrared, or other wireless media. The term computer-readable media asused herein includes both storage media and communication media.Input/output system 306 also may include an input device 310 such askeyboard, mouse, pen, voice input device, or touch input device.Input/output system 306 may also include an output device 312, such as adisplay, speakers, or a printer.

Processor 302 may be capable of performing functions associated withtelecommunications, such as functions for processing broadcast messages,as described herein. For example, processor 302 may be capable of, inconjunction with any other portion of network device 300, determining atype of broadcast message and acting according to the broadcast messagetype or content, as described herein.

Memory 304 of network device 300 may comprise a storage medium having aconcrete, tangible, physical structure. As is known, a signal does nothave a concrete, tangible, physical structure. Memory 304, as well asany computer-readable storage medium described herein, is not to beconstrued as a signal. Memory 304, as well as any computer-readablestorage medium described herein, is not to be construed as a transientsignal. Memory 304, as well as any computer-readable storage mediumdescribed herein, is not to be construed as a propagating signal. Memory304, as well as any computer-readable storage medium described herein,is to be construed as an article of manufacture.

Memory 304 may store any information utilized in conjunction withtelecommunications. Depending upon the exact configuration or type ofprocessor, memory 304 may include a volatile storage 314 (such as sometypes of RAM), a nonvolatile storage 316 (such as ROM, flash memory), ora combination thereof. Memory 304 may include additional storage (e.g.,a removable storage 318 or a non-removable storage 320) including, forexample, tape, flash memory, smart cards, CD-ROM, DVD, or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, USB-compatible memory, or any othermedium that can be used to store information and that can be accessed bynetwork device 300. Memory 304 may comprise executable instructionsthat, when executed by processor 302, cause processor 302 to effectuateoperations to map signal strengths in an area of interest.

FIG. 6 depicts an exemplary diagrammatic representation of a machine inthe form of a computer system 500 within which a set of instructions,when executed, may cause the machine to perform any one or more of themethods described above. One or more instances of the machine canoperate, for example, as processor 302, UE 101, UE 103, UE 104, basestation 102, server 105, and other devices of FIG. 1, FIG. 4, and FIG.5. In some examples, the machine may be connected (e.g., using a network502) to other machines. In a network deployment, the machine may operatein the capacity of a server or a client user machine in a server-clientuser network environment, or as a peer machine in a peer-to-peer (ordistributed) network environment.

The machine may comprise a server computer, a client user computer, apersonal computer (PC), a tablet, a smart phone, a laptop computer, adesktop computer, a control system, a network router, switch or bridge,or any machine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. It will beunderstood that a communication device of the subject disclosureincludes broadly any electronic device that provides voice, video ordata communication. Further, while a single machine is illustrated, theterm “machine” shall also be taken to include any collection of machinesthat individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methods discussed herein.

Computer system 500 may include a processor (or controller) 504 (e.g., acentral processing unit (CPU)), a graphics processing unit (GPU, orboth), a main memory 506 and a static memory 508, which communicate witheach other via a bus 510. The computer system 500 may further include adisplay unit 512 (e.g., a liquid crystal display (LCD), a flat panel, ora solid state display). Computer system 500 may include an input device514 (e.g., a keyboard), a cursor control device 516 (e.g., a mouse), adisk drive unit 518, a signal generation device 520 (e.g., a speaker orremote control) and a network interface device 522. In distributedenvironments, the examples described in the subject disclosure can beadapted to utilize multiple display units 512 controlled by two or morecomputer systems 500. In this configuration, presentations described bythe subject disclosure may in part be shown in a first of display units512, while the remaining portion is presented in a second of displayunits 512.

The disk drive unit 518 may include a tangible computer-readable storagemedium on which is stored one or more sets of instructions (e.g.,software 526) embodying any one or more of the methods or functionsdescribed herein, including those methods illustrated above.Instructions 526 may also reside, completely or at least partially,within main memory 506, static memory 508, or within processor 504during execution thereof by the computer system 500. Main memory 506 andprocessor 504 also may constitute tangible computer-readable storagemedia.

As described herein, a telecommunications system may utilize a softwaredefined network (SDN). SDN and a simple IP may be based, at least inpart, on user equipment, that provide a wireless management and controlframework that enables common wireless management and control, such asmobility management, radio resource management, QoS, load balancing,etc., across many wireless technologies, e.g. LTE, Wi-Fi, and 5G accesstechnologies; decoupling the mobility control from data planes to letthem evolve and scale independently; reducing network state maintainedin the network based on user equipment types to reduce network cost andallow massive scale; shortening cycle time and improving networkupgradability; flexibility in creating end-to-end services based ontypes of user equipment and applications, thus improve customerexperience; or improving user equipment power efficiency and batterylife-especially for simple M2M devices-through enhanced wirelessmanagement.

While examples of a system in which site estimation and crowd sourceddata can be processed and managed have been described in connection withvarious computing devices/processors, the underlying concepts may beapplied to any computing device, processor, or system capable offacilitating a telecommunications system. The various techniquesdescribed herein may be implemented in connection with hardware orsoftware or, where appropriate, with a combination of both. Thus, themethods and devices may take the form of program code (i.e.,instructions) embodied in concrete, tangible, storage media having aconcrete, tangible, physical structure. Examples of tangible storagemedia include floppy diskettes, CD-ROMs, DVDs, hard drives, or any othertangible machine-readable storage medium (computer-readable storagemedium). Thus, a computer-readable storage medium is not a signal. Acomputer-readable storage medium is not a transient signal. Further, acomputer-readable storage medium is not a propagating signal. Acomputer-readable storage medium as described herein is an article ofmanufacture. When the program code is loaded into and executed by amachine, such as a computer, the machine becomes a device fortelecommunications. In the case of program code execution onprogrammable computers, the computing device will generally include aprocessor, a storage medium readable by the processor (includingvolatile or nonvolatile memory or storage elements), at least one inputdevice, and at least one output device. The program(s) can beimplemented in assembly or machine language, if desired. The languagecan be a compiled or interpreted language, and may be combined withhardware implementations.

The methods and devices associated with a telecommunications system asdescribed herein also may be practiced via communications embodied inthe form of program code that is transmitted over some transmissionmedium, such as over electrical wiring or cabling, through fiber optics,or via any other form of transmission, wherein, when the program code isreceived and loaded into and executed by a machine, such as an EPROM, agate array, a programmable logic device (PLD), a client computer, or thelike, the machine becomes a device for implementing telecommunicationsas described herein. When implemented on a general-purpose processor,the program code combines with the processor to provide a unique devicethat operates to invoke the functionality of a telecommunicationssystem.

While the disclosed systems have been described in connection with thevarious examples of the various figures, it is to be understood thatother similar implementations may be used or modifications and additionsmay be made to the described examples of a telecommunications systemwithout deviating therefrom. For example, one skilled in the art willrecognize that a telecommunications system as described in the instantapplication may apply to any environment, whether wired or wireless, andmay be applied to any number of such devices connected via acommunications network and interacting across the network. Therefore,the disclosed systems as described herein should not be limited to anysingle example, but rather should be construed in breadth and scope inaccordance with the appended claims.

In describing preferred methods, systems, or apparatuses of the subjectmatter of the present disclosure—site estimation and crowd sourceddata—as illustrated in the Figures, specific terminology is employed forthe sake of clarity. The claimed subject matter, however, is notintended to be limited to the specific terminology so selected. Inaddition, the use of the word “or” is generally used inclusively unlessotherwise provided herein.

This written description uses examples to enable any person skilled inthe art to practice the claimed subject matter, including making andusing any devices or systems and performing any incorporated methods.Other variations of the examples are contemplated herein.

Methods, systems, and apparatuses, among other things, as describedherein may provide for means for site estimation using crowd sourceddata. A method, system, computer readable storage medium, or apparatusreceiving data associated with a location of plurality of devices thathave connected with a base station in a geographic area; determining amachine learning model to apply to the received data based on the typeof data, wherein the machine learning model is a clustering model;performing the clustering model on the received data; based on theperforming the clustering model on the received data, obtainingrepresentative data that excludes outliers in the received data; basedon the representative data, determining a location of the base stationin the geographic area; and sending a message with the location of thebase station. The location may be determined by using trilateration. Thelocation of the base station may be provided to a visual mappingapplication. The crowd sourced data may include anonymized data fromapplications of the plurality of devices. The crowd sourced data mayinclude timing advance data, radio frequency data, DL or UL throughputdata. The clustering model may include density-based spatial clusteringof applications with noise (DBSCAN), K-Means Clustering, Mean-ShiftClustering, Expectation-Maximization (EM) Clustering using GaussianMixture Models (GMM), or Agglomerative Hierarchical Clustering. Allcombinations in this paragraph (including the removal or addition ofsteps) are contemplated in a manner that is consistent with the otherportions of the detailed description.

What is claimed:
 1. An apparatus comprising: a processor; and memorycoupled with the processor, the memory storing executable instructionsthat when executed by the processor cause the processor to effectuateoperations comprising: receiving crowd sourced data associated with alocation of a plurality of devices that have connected with a basestation in a geographic area, the crowd sourced data including at leastsome inaccurate information including one or more of incorrect cellidentity information, or inaccurate time advance measurementinformation; determining a machine learning model to apply to thereceived crowd sourced data based on the type of data, wherein themachine learning model is a clustering model; performing the clusteringmodel on the received crowd sourced data to filter out the at least someinaccurate information; filtering out additional inaccurate informationbased on radio frequency information including downlink throughput,uplink throughput and latency information for the plurality of devicesthat have connected with the base station, obtaining representative datathat excludes the at least some inaccurate information from the receivedcrowd sourced data; based on the representative data, determining alocation of the base station in the geographic area; and sending amessage with the location of the base station.
 2. The apparatus of claim1, wherein the location is determined by using trilaterationcalculation.
 3. The apparatus of claim 1, wherein the location of thebase station is provided to a visual mapping application.
 4. Theapparatus of claim 1, wherein the crowd sourced data comprisesanonymized data from applications of the plurality of devices.
 5. Theapparatus of claim 1, wherein the crowd sourced data comprises timingadvance data.
 6. The apparatus of claim 1, wherein the crowd sourceddata comprises radio frequency data.
 7. The apparatus of claim 1,wherein the crowd sourced data comprises downlink throughput data oruplink throughput data.
 8. The apparatus of claim 1, wherein theplurality of devices comprises user equipment.
 9. The apparatus of claim1, wherein the clustering model comprises density-based spatialclustering of applications with noise (DB SCAN), K-Means Clustering,Mean-Shift Clustering, Expectation-Maximization (EM) Clustering usingGaussian Mixture Models (GMM), or Agglomerative Hierarchical Clustering.10. A non-transitory computer readable storage medium storing computerexecutable instructions that when executed by a computing device causesaid computing device to effectuate operations comprising: receivingcrowd sourced data associated with a location of a plurality of devicesthat have connected with a base station in a geographic area, the crowdsourced data including at least some inaccurate information includingone or more of incorrect cell identity information, or inaccurate timeadvance measurement information; determining a machine learning model toapply to the received crowd sourced data, wherein the machine learningmodel is a clustering model; performing the clustering model on thereceived crowd sourced data to filter out the at least some inaccurateinformation; filtering out additional inaccurate information based onradio frequency information including downlink throughput, uplinkthroughput and latency information for the plurality of devices thathave connected with the base station, obtaining representative data thatexcludes the at least some inaccurate information from the receivedcrowd sourced data; based on the representative data, determining alocation of the base station in the geographic area; and sending amessage with the location of the base station.
 11. The non-transitorycomputer readable storage medium of claim 10, wherein the location isdetermined by using trilateration.
 12. The non-transitory computerreadable storage medium of claim 10, wherein the location of the basestation is provided to a visual mapping application.
 13. Thenon-transitory computer readable storage medium of claim 10, wherein thecrowd sourced data comprises anonymized data from applications of theplurality of devices.
 14. The non-transitory computer readable storagemedium of claim 10, wherein the crowd sourced data comprises timingadvance data.
 15. The non-transitory computer readable storage medium ofclaim 10, wherein the crowd sourced data comprises radio frequency data.16. The non-transitory computer readable storage medium of claim 10,wherein the crowd sourced data comprises downlink throughput data oruplink throughput data.
 17. The non-transitory computer readable storagemedium of claim 10, wherein the plurality of devices comprises userequipment.
 18. The non-transitory computer readable storage medium ofclaim 10, wherein the clustering model comprises K-Means Clustering,Mean-Shift Clustering, Expectation-Maximization (EM) Clustering usingGaussian Mixture Models (GMM), or Agglomerative Hierarchical Clustering.19. A system comprising: one or more processors; and memory coupled withthe one or more processors, the memory storing executable instructionsthat when executed by the one or more processors cause the one or moreprocessors to effectuate operations comprising: receiving crowd sourceddata associated with a location of a plurality of devices that haveconnected with a base station in a geographic area, the crowd sourceddata including inaccurate information including one or more of incorrectcell identity information, or inaccurate time advance measurementinformation; determining a machine learning model to apply to thereceived crowd sourced data, wherein the machine learning model is aclustering model; performing the clustering model on the received crowdsourced data to filter out the inaccurate information from the crowdsourced data; filtering out additional inaccurate information based onradio frequency information including downlink throughput, uplinkthroughput and latency information for the plurality of devices thathave connected with the base station, forming representative data thatexcludes the inaccurate information from the received crowd sourceddata; based on the representative data, determining a location of thebase station in the geographic area; and sending a message with thelocation of the base station.
 20. The system of claim 19, wherein thecrowd sourced data comprises radio frequency data.